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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah INTEK: Informatika dan Teknologi Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Applied Technology and Computing Science Journal Infotekmesin Jurnal Sistem Informasi dan Informatika (SIMIKA) Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Media Aplikom Jurnal Pengabdian Teknologi Tepat Guna Jurnal Nasional Teknik Elektro dan Teknologi Informasi Wahana
Muhammad Nur Faiz
Politeknik Negeri Cilacap
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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A Comparative Analysis of KIP-K Acceptance Prediction Based on School Type Using XGBoost, Random Forest, and SVM-RBF: Evaluation Through Accuracy and Data Visualization Riyadi Purwanto; Fajar Mahardika; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/10.35970/jinita.v7i2.2948

Abstract

The Indonesia Smart College Card (Kartu Indonesia Pintar-Kuliah / KIP-K) is a national initiative aimed at expanding access to higher education for students from socioeconomically disadvantaged backgrounds. This study, conducted at Politeknik Negeri Cilacap, investigates the prediction of KIP-K acceptance based on the type of high school attended by applicants. A comparative analysis was carried out using three supervised machine learning algorithms: Extreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Machine with Radial Basis Function (SVM-RBF). The dataset, sourced from institutional admission records between 2022 and 2024, comprises information on school types (public, private, vocational, madrasah, and others), demographic attributes, and the KIP-K acceptance status. The data were split into training and testing sets using a 50:50 stratified sampling technique to preserve class distribution. Model performance was evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score. Additionally, confusion matrices, ROC curves, and feature importance visualizations were used to enhance model interpretability. The experimental results demonstrate that the XGBoost algorithm consistently outperformed the other models across all performance metrics. Specifically, XGBoost exhibited the highest discriminatory power with an AUC of 0.93, followed by Random Forest (0.90) and SVM-RBF (0.85). These findings affirm the suitability of tree-based ensemble methods for classification tasks in educational domains and emphasize the predictive relevance of school type in determining KIP-K eligibility. The study presents a data-driven decision support framework that can contribute to more objective, transparent, and equitable scholarship allocation practices, particularly within the context of vocational higher education institutions in Indonesia
Development of a Hybrid CNN–SVM-Based Acute Lymphoblastic Leukemia Detection System on Hematology Image Data Linda Perdana Wanti; Annisa Romadloni; Kukuh Muhammad; Abdul Rohman Supriyono; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.3002

Abstract

Acute Lymphoblastic Leukemia (ALL) is among the most common pediatric blood cancers and progresses rapidly, necessitating early and accurate detection. Manual diagnosis via microscopic analysis of blood samples is time-consuming and highly dependent on specialist expertise. This study proposes a hybrid model that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM) to automatically detect ALL from blood-cell images. The CNN performs deep feature extraction from images, while the SVM serves as the classifier to determine ALL status. The dataset comprises microscopic images labeled as ALL or normal and is processed through preprocessing steps such as augmentation and normalization. The adopted CNN produces optimized feature representations. Experimental results show that the hybrid CNN–SVM model with an RBF kernel achieves the best performance, with an accuracy of 96.4%, precision of 95.8%, recall of 96.1%, and an F1-score of 96.0%, surpassing pure CNN-based baselines. Training converged at the 41st epoch, with a training accuracy of 97.2%, validation accuracy of 95.9%, training loss of 0.09, and validation loss of 0.11, indicating stable learning without overfitting. The model’s ROC curve lies well above the chance diagonal, with an Area Under the Curve (AUC) of 0.914, means there is a 91.4% chance the model assigns a higher score to a truly positive (leukemia) image than to a negative (normal) image.These findings suggest that the CNN–SVM hybrid approach enhances leukemia detection performance compared with conventional CNN-only methods and holds promise as a fast, accurate, and efficient image-based decision-support tool for early leukemia diagnosis in digital hematology.
Peningkatan Kapasitas UMKM Kabupaten Cilacap Melalui Kecerdasan Buatan dan Keamanan Transaksi Digital Bella Adinda Putri; Linda Perdana Wanti; Satriawan Desmana; Krisna Nuresa Qodri; Ratih Ratih; Abdul Rohman Supriyono; Muhammad Nur Faiz; Oman Somantri; Ratih Hafsarah Maharrani
Wahana Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 1 (2026): Edisi Juni
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/wahana.v5i1.1778

Abstract

UMKM di era digital menghadapi tantangan karena kurangnya pemahaman tentang literasi digital dan kesadaran akan ancaman siber. Kegiatan ini bertujuan untuk meningkatkan pemahaman pelaku UMKM–yang didukung oleh BAZNAS Kabupaten Cilacap–dalam memanfaatkan teknologi AI dan aplikasi keamanan digital. Metode pelaksanaannya menggunakan pelatihan praktik langsung, diskusi interaktif, dan pendampingan langsung melalui dua tahap: (1) pengenalan konsep AI untuk keamanan transaksi digital; dan (2) pelatihan tentang aplikasi pendukung keamanan digital (GetContact, Kredibel, dan VirusTotal). Kegiatan ini melibatkan 100 pelaku UMKM dari berbagai sektor usaha. Evaluasi yang dilakukan melalui pre-test dan post-test menggunakan 10 pertanyaan terkait materi pelatihan menunjukkan peningkatan yang signifikan dengan rata-rata skor peserta meningkat dari 78,09 (pre-test) menjadi 97,50 (post-test), yang menggambarkan peningkatan sebesar 19,41 poin atau 28,98%. Hal ini menunjukkan bahwa pelatihan tersebut efektif meningkatkan literasi keamanan digital dan memberdayakan pelaku UMKM untuk melindungi bisnis mereka dari ancaman kejahatan siber dan penipuan online.
Optimized Skill Mastery Prediction for Adaptive Test Decision-Making Linda Perdana Wanti; Rujianto Eko Saputro; Fandy Setyo Hutomo; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3171

Abstract

Adaptive testing systems require accurate and timely estimation of students' skill mastery levels to support decision-making in question selection and assessment flow determination. However, uncertainty in students' knowledge levels and variations in learning behavior often limit the performance of conventional predictive models. This study proposes an optimized predictive modeling framework for skill mastery to enhance decision-making quality in adaptive testing. The proposed framework integrates a machine learning-based skill mastery prediction model with an optimization mechanism to improve model accuracy and stability, while accommodating the sequential nature and uncertainty of student responses. Learning interaction data is used to dynamically model the development of skill mastery levels, which are then utilized as decision-support input in the adaptive testing system. The proposed predictive skill mastery model shows strong and consistent performance with an AUC value of 0.822, Average Precision of 0.868, accuracy of 0.757, and a precision balance of 0.834, recall of 0.788, and F1-score of 0.810, supported by well-calibrated probabilities and the ability to respond adaptively to student learning dynamics, making it suitable for use to support decision-making in adaptive test decision-making systems. The results of this study confirm the potential of integrating predictive analytics and optimization techniques in developing intelligent adaptive assessment systems.
A Reproducible Explainable NLP Workflow for Workplace Sexism Detection: Classification Performance, Rationale Faithfulness, and Sanity Checks Annisa Romadloni; Linda Perdana Wanti; Laura Sari; Muhammad Nur Faiz; Qisthi Alhazmi Hidayaturrohman
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3222

Abstract

Workplace sexism often appears as indirect, deniable language (e.g., patronizing compliments, competence-doubting questions), making automated detection and organizational response difficult. This study evaluates a transparent, explanation-ready NLP pipeline on the Sexist Workplace Statements (SWS) dataset (1,137 items) with its binary labels: certain sexism vs. ambiguous/neutral. Using the provided fixed stratified split (1,023 train; 114 test), we train a TF–IDF (word 1–2, character 3–5 n-grams) logistic regression baseline and report performance stability across five random seeds. To audit model evidence, sparse token rationales are extracted from linear feature contributions and quantify faithfulness with ERASER-style comprehensiveness (logit drop when rationales are removed) and sufficiency (logit change when only rationales are kept), benchmarked against random-token rationales. The baseline achieves 0.768 ± 0.006 accuracy and 0.759 ± 0.007 macro-F1, with errors concentrated in the ambiguous/neutral class. Faithfulness tests show that model-selected rationales substantially affect the sexism logit (comprehensiveness 1.335 ± 0.001), while remaining insufficient in isolation (|sufficiency| 1.075 ± 0.006). Sanity checks reveal modest sensitivity to gender-term swaps and reduced rationale overlap underweight randomization. Overall, results motivate cautious deployment: explanation-driven auditing can surface shortcut risks and clarify where binary labels blur neutral language and deniable sexism, pointing to future work on finer-grained annotation and human rationale collection.
Co-Authors Abdul Fadlil Abdul Rohman Supriyono Achmad Zaki Yamani Agus Susanto Agus Susanto Andy Victor Pakpahan Anggita Dwi Fatimah Annas Setiawan Prabowo Annisa Romadloni Annisa Romadloni Anton Yudhana Ari Kristiningsih Arif Wirawan Muhammad Bella Adinda Putri Davito Rasendriya Rizqullah Putra Dwi Cita Septia Candra Erna Alimudin Fahmi Reza Ferdiansyah Fandy Setyo Hutomo Ganjar Ndaru Ikhtiagung Gautama Wijaya Haeruddin, . Hani Hidayati Hani Hidayati Hendra Winata Hety Dwi Astuti Ifda Nurul Azroha Imam Riadi Isa Bahroni, Isa Khoeruddin Wittriansyah Krisna Nuresa Qodri Kukuh Muhammad Laura Sari Laura Sari Linda Perdana Wanti Linda Perdana Wanti Lutfi Syafirullah Lutfi Syarifullah Mahardika, Fajar Nur Chasanah Nur Wachid Adi Prasetya Nur Wahyu Rahadi Oman Somantri Prih Diantono Abda`u Qisthi Alhazmi Hidayaturrohman Ratih Hafsarah Maharrani Ratih Ratih Riyadi Purwanto Robby Gustian Rujianto Eko Saputro Rusydi Umar Santi Purwaningrum Santi Purwaningrum Satriawan Desmana Seppy Ayu Rachmawati Sidiq, Muhammad Fajar Sukma Aji Ummi Athiyah Wahyu Adi Prabowo